Autenticação Biométrica Baseada em PPG e ECG utilizando Aprendizado Profundo

  • Eduardo T. Tristão Universidade Federal de Viçosa
  • Kristtopher K. Coelho Universidade Federal de Viçosa
  • Edelberto Franco Silva Universidade Federal de Juiz de Fora
  • Alex B. Vieira Universidade Federal de Juiz de Fora
  • Michele Nogueira Universidade Federal de Minas Gerais
  • José Augusto M. Nacif Universidade Federal de Viçosa

Resumo


A popularização da Internet das Coisas aumentou significativamente os requisitos para a transmissão e armazenamento de dados pessoais sensíveis. Consequentemente, esses avanços exigem políticas rígidas de controle de acesso com a necessidade de garantir segurança e privacidade de forma eficaz. É possível encontrar na literatura que a autenticação biométrica baseada em sinais de PPG (fotopletismografia) ou ECG (eletrocardiograma) são potenciais suportes no atendimento a esses requisitos. Pensando nisso, este artigo propõe um método de identificação multimodal de indivíduos, combinando ambos os sinais. Nossa proposta combina duas redes neurais convolucionais em cascata, dando avanços ao estado da arte. Como resultados numéricos, o método atinge 99,62% de acurácia, 93,83% de precisão e 0,04% FAR em diferentes bases de dados.

Palavras-chave: Autenticação Biométrica, Sinais Biométricos, ECG, PPG, Aprendizado Profundo

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Publicado
21/11/2022
TRISTÃO, Eduardo T.; COELHO, Kristtopher K.; SILVA, Edelberto Franco; VIEIRA, Alex B.; NOGUEIRA, Michele; NACIF, José Augusto M.. Autenticação Biométrica Baseada em PPG e ECG utilizando Aprendizado Profundo. In: ARTIGOS COMPLETOS - SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 12. , 2022, Fortaleza/CE. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 25-31. ISSN 2763-9002. DOI: https://doi.org/10.5753/sbesc_estendido.2022.227273.